Learning data representations that are transferable and are fair with respect
to certain protected attributes is crucial to reducing unfair decisions while
preserving the utility of the data. We propose an information-theoretically
motivated objective for learning maximally expressive representations subject
to fairness constraints. We demonstrate that a range of existing approaches
optimize approximations to the Lagrangian dual of our objective. In contrast to
these existing approaches, our objective allows the user to control the
fairness of the representations by specifying limits on unfairness. Exploiting
duality, we introduce a method that optimizes the model parameters as well as
the expressiveness-fairness trade-off. Empirical evidence suggests that our
proposed method can balance the trade-off between multiple notions of fairness
and achieves higher expressiveness at a lower computational cost.

本文提出了一种信息理论动机的目标函数，用于学习在公平约束条件下最具表达力的数据表示，旨在通过控制不公平性极限的方式帮助用户控制数据表示的公平性，证明了现有方法无法完全实现此目标，通过引入一种优化模型参数和表达力 - 公平性权衡的方法来平衡多种公平概念的权衡，并在更低的计算成本下实现更高的表达能力。